Mining time-dependent gene features.

Abstract

This paper presents an application of the Independent Component Analysis (ICA) method to genomic data. In particular, experimentally produced perturbation effects over the E.coli bacterium are monitored through the changes of gene expression values observed at regular times, and until steady state has been reached. The aim is to control the response of the SOS system to DNA damage. We might assume that only part of the genetic regulatory network is affected directly by the perturbation conditions, as indirect cascade effects might also be present, and some genes may change just because of randomness. ICA decomposes the gene matrix and identifies groups of genes belonging to a certain estimated component by virtue of co-expression; it is of course of interest to establish co-regulation dynamics, which might underlie the captured correlation. Stronger forms of dependence, like Mutual Information, are thus computed and compared with linear correlation in order to validate the results and establish the role of the identified components in determining the network dynamics.